Point Cloud Neighborhood Estimation Method Using Deep Neuro-Evolution

Ahmed Abouelazm, Igor Vozniak, Nils Lipp, Pavel Astreika, Christian Mueller

2023

Abstract

Due to the recent advancements in computing hardware, deep learning, and 3D sensors, point clouds have become an essential 3D data structure, and their processing and analysis have received considerable attention. Given the unstructured and irregular nature of point clouds, encoding local geometries is a significant barrier in point cloud analysis. The aforementioned challenge is known as neighborhood estimation, and it is commonly addressed by fitting a plane to points within a local neighborhood defined by estimated parameters. The estimated neighborhood parameters for each point should adapt to the point cloud’s irregularities and different local geometries’ sizes and shapes. Different objective functions have been derived in the literature for optimal parameters selection with no efficient approach for these objective functions’ optimization as of now. In this work, we propose a novel neighborhood estimation pipeline for such optimization which is objective function and neighborhood type invariant, utilizing a modified version of deep Neuro-Evolution algorithm and Farthest Point Sampling as an intelligent sampling approach. Results demonstrate the ability of the proposed pipeline for state-of-the-art objective functions optimization and enhancement of neighborhood properties estimation such as the normal vector.

Paper Citation

in Harvard Style

Abouelazm A., Vozniak I., Lipp N., Astreika P. and Mueller C. (2023). Point Cloud Neighborhood Estimation Method Using Deep Neuro-Evolution. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 582-593. DOI: 10.5220/0011627300003417

in Bibtex Style

@conference{visapp23,
author={Ahmed Abouelazm and Igor Vozniak and Nils Lipp and Pavel Astreika and Christian Mueller},
title={Point Cloud Neighborhood Estimation Method Using Deep Neuro-Evolution},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={582-593},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011627300003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Point Cloud Neighborhood Estimation Method Using Deep Neuro-Evolution
SN - 978-989-758-634-7
AU - Abouelazm A.
AU - Vozniak I.
AU - Lipp N.
AU - Astreika P.
AU - Mueller C.
PY - 2023
SP - 582
EP - 593
DO - 10.5220/0011627300003417
PB - SciTePress